1. Field of the Invention
The present invention relates generally to optical measurement devices, and, more particularly, to pulse oximetry systems with a novel method of dynamically calibrating a pulse oximeter based upon empirical inputs and a related parameter that is a function of the DC component commonly measured in pulse oximetry.
2. Description of the Related Art
Oximetry is based on the principle that the color of blood is related to the oxygen saturation level of hemoglobin. For example, as blood deoxygenates, skin loses its pinkish appearance and takes on more of a bluish tint. This principle permits measurement of the degree of oxygen saturation of blood using what is commonly known as optical pulse oximetry technology.
Optical oximeters take advantage of the fact that oxygenated and deoxygenated hemoglobin absorb visible and near infrared light differently. Generally, blood perfused tissue is illuminated and light absorption by the tissue is determined by a suitable light sensor. The light absorption is then correlated with an estimated oxygen saturation level (SaO2). In commonly used methods of pulse oximetry, the blood perfused tissue is illuminated by light selected to have at least two different wavelengths, preferably one in the red band and one in the infrared band.
A distinct absorption corresponds to each wavelength of light, such that a specific absorption corresponds to each hemoglobin oxygen saturation value in the range 0-100%. See e.g., Physio-optical considerations in the design of fetal pulse oximetry sensors, Mannheimer et al., European Journal of Obstetrics & Gynecology and Reproductive Biology, 72 Suppl. 1 (1997). Accurate oximeter performance requires a good overlap of light penetration in tissue at the chosen wavelengths so as to minimize the effects of tissue heterogenicity.
Pulse oximeter oxygen saturation level readings are denoted by SpO2, whereas oxygen saturation in arterial blood samples based on direct in vitro measurement are denoted SaO2. The pulse oximetry oxygen saturation level (SpO2) is determined by positioning the blood-perfused tissue adjacent to a light source and a detector, passing a light of each of two wavelengths through the tissue, measuring the constant and pulsatile light intensities at each wavelength, and correlating them to an SpO2 reading.
Values of light absorption measured in pulse oximetry generally include a constant (non-pulsatile) component and a variable (pulsatile) component. The constant component is commonly referred to as the “DC” component. The measured DC component is influenced by several factors, such as the light absorbency of the biological tissue, the presence of venous blood, capillary blood, and non-pulsatile arterial blood, the scattering properties of tissue, the intensity of the light source, and the sensitivity of the detector.
The variable component results from the pulsatile flow of arterial blood through the tissue being probed. This pulsatile flow, corresponding to the systole phase of the cardiac cycle, acts such that light absorption varies proportionately to the flow of blood. This variable absorption of light through tissue (the pulsatile component) is commonly referred to as the “AC” component. Because pulsing is a function of the fluctuating volume of arterial blood, the AC light intensity level fairly represents the light absorption of the oxygenated and deoxygenated hemoglobin of arterial blood.
To determine a ratio (R) of pulsatile light intensities to non-pulsatile light intensities, the constant DC component of the light intensity must be factored out. The amplitudes of both the AC and DC components are dependent on the incident light intensity. Dividing the AC level by the DC level gives a “corrected” AC level that is no longer a function of the incident light intensity. Thus, ratio R=(AC1/DC1)/(AC2/DC2) is an indicator of arterial SaO2. Conventionally, an empirically derived calibration curve for the relationship between the above ratio R and SaO2 provides the pulse oximetry oxygen saturation level SpO2.
In oximetry, the measured transmission of light traveling through blood-perfused tissue, and the pulse oximetry oxygen saturation level (SpO2), are therefore based upon two things: one, the natural difference in light absorption in oxygenated hemoglobin and deoxygenated hemoglobin; and two, the detected change in light absorption resulting from the fluctuating volume of arterial blood passing through the tissue between the light source and the sensor, i.e., the pulsatile component. The amplitude of the pulsatile component is a small fraction of the total signal amplitude, so small changes in the pulsatile component may be “lost” in the background of the total signal amplitude.
By relying on the pulsatile component in this manner, current pulse oximeters and methodologies cannot effectively account for light scattering and absorption of light in the biological tissues that are being probed. Thus, current techniques use empirical data and factor in an average component for scattering and absorption. See e.g., Pulse Oximetry: Theoretical and Experimental Models. De Kock, et al., Medical and Biological Engineering & Computing, Vol. 31, (1993). This approach results in oximeters that rely upon fixed calibration curves to predict SpO2 from measured electronic signals.
The current practice in pulse oximetry of subsuming the scattering and absorption of light that occurs in tissue by resorting to empirical calibration techniques is problematic. While it may be acceptable at oxygen saturation levels within normal ranges for adults, i.e., 70% to 100% SaO2, it becomes less acceptable when oxygen saturation is in the lower range, for example, of 15% to 65% SaO2. This lower range represents severe hypoxia in post-natal subjects, and is also commonly encountered in fetal oximetry. Both of these circumstances require accurate and reliable oxygen saturation estimates.
In oximeters with larger probes, e.g., probes having a pathlength between the emitter and detector that would encompass a finger, foot or earlobe, the conventional approach to calibration is acceptable because scatter and absorption are less of an issue. As the probe size decreases, however, and the pathlength becomes shorter, e.g., fetal oximeter probes, the error due to background scattering and absorption has a relatively greater impact on oximeter accuracy.
Precise estimation of SpO2 with probes having a pathlength less than 5 mm is difficult due to the scattering and absorption of light in tissue. The challenge, therefore, is to account for scattering and absorption through their relationship to the measured DC and AC signals.
Approaches have been described in the literature wherein the scattering and absorption characteristics of the tissue being probed are theoretically modeled. See e.g., Diffusion-based model of pulse oximetry: in vitro and in vivo comparisons, Marble et al., Applied Optics, Vol.33, No. 7 (1994); Pulse Oximetry: Theoretical and Experimental Models, Kock et al., Med. & Biol. Eng. & Comput., Vol. 31 (1993). One problem with the theoretical approach is that the total number of variables used in the various models make it difficult to accurately model these characteristics. This results in further approximations, and in an inevitable “guessing” of some of the parameters. For example, in order to calculate absorption from the DC signal, one has to guess scattering. Similarly, where one wants to calculate scattering from the DC signal, absorption has to be approximated.
Furthermore, inter-patient and intra-patient variation between the biological tissues that are probed, present a significant challenge to the purely theoretical approach. This variation precludes the modeling of scattering and absorption in a dynamic fashion. Neither the currently employed empirical approach, nor the theoretical models currently described, are as accurate or dynamic as the calibration techniques of the present invention.
The present invention differs from conventional techniques in that it does not use an arbitrary guess for scattering, but instead uses clinical data to evaluate an average scattering, and incorporates that value into a parameter identified as kDC. In particular, the functional dependence of kDC on the measured signals AC and DC depends on the average scattering which is derived from the clinical studies.